3D indoor modeling is of great significance for various application fields, and thus various 3D laser scanning devices have been derived. These devices continuously project laser pulses to obtain the point cloud data of the target indoor environment and try to rely on these point cloud data to build a 3D model of the target indoor environment.
The 3D modeling speed in an ideal indoor environment (low complexity) can basically meet the demand, but the actual indoor environment usually has a higher complexity. All kinds of furniture items may block each other and may be placed in disorder, and there may be some special, complex constructions, thereby increasing the difficulty of building 3D models based on raw point cloud data (raw dataset). For example, there may be some difficulties in determining structural elements (walls, ceiling and floor) or wall surface-objects (windows, doors and other openings) in indoor scenes. These difficulties include disorder and blocking problems caused by furniture that may block some walls or windows, as well as messy conditions in offices or apartments. Some rooms are laid out according to the regular shape of the orthogonal boundaries, but some rooms are not laid out like this, causing the complexity of the room layout.
The complexity of the indoor environment needs to be solved with different methods according to different situations. For example, the Manhattan hypothesis only involves the detection of orthogonal structures, and the other methods are to obtain the prior knowledge of the objects in the target indoor environment in advance and use the prior knowledge to provide guidance for the actual building of a 3D model. The prior knowledge is analogous to having a teacher who knows the answer and guides the student to answer the question. However, this is rarely the situation in which prior knowledge is normally unavailable. Accordingly, there is a need in the art to develop a 3D indoor modeling method for complex indoor scenes efficiently by using the raw point cloud data only without any prior knowledge.